Changes In Market Correlation Vitaliy Paykov 212215
31 January 2014 - 3 minute read

Here we analyse the correlation structure between futures markets, S&P 500 stocks and trend-following strategies over the past 50 years. In this context, the recent crisis stands out as a prolonged period of heightened activity across asset classes.

We analyse 20 futures markets from four different sectors. We use the front contract and have restricted ourselves to markets traded within the US to ensure the settlement prices are approximately synchronous.

Table 1: Futures markets in scope

Table 1

We consider the average absolute correlation between all 20 contracts, and then between those within the same sector, or in different sectors [1]. We display the results from 1973, which is when more than half of the markets have started trading.

Figure 1: Correlation across futures markets, within sectors and between sectors, from 1973

Figure 1

Up until 2008, we find that the average absolute correlation has been fairly stable, with moderate jumps during the early 1980s recession, the 1997 Asian financial crises and the 2002 stock market downturn among others.

Since the Lehman collapse in 2008, we can see an increase, with correlation staying above 30% over a prolonged period of five years. More recently, correlation appears to have returned to “business as usual”, tending towards pre-crisis levels.

The correlation between assets in different sectors follows a similar pattern. This is unsurprising since there are many more between-sector relationships and so this data dominates the all assets result. This increase is present between nearly all sectors, but the most widely publicised change is between commodities and equity indices. While commodities have previously been regarded as a good diversifying asset class to an equities portfolio, this perception has changed in the recent crisis when correlations hit an all-time high. However, as before, this feature appears to be a temporary “blip”.

The correlation between futures within a sector has seen a different trend, with a slow rise over the last 40 years, and less of a marked change during the recent crisis. Markets have become increasingly electronic in the last 20 years, and a potential explanation is that this has created tighter linkages which drive this rise in correlation. Were this true, it would follow that the trend is unlikely to reverse.


We use the constituents of the S&P 500 Index to represent US equities. For each pair of stocks we assess the correlation from the beginning of trading, but only include the figure when both stocks are in the index.

Figure 2: Correlation between futures market, S&P 500 stocks and trend-following systems

Figure 2

Compared to futures, correlations in equities are a lot more erratic in times of crises. In the late 1980s, for example, Black Monday resulted in a sharp jump with average correlation peaking at about 60%. The jumps during the 1997 Asian crisis, 1998 Russian financial crisis and the 2002 stock market downturn are also more pronounced. The credit crisis in 2008 is an unusual case – as is the case in futures markets, we see increased correlation over a sustained period.

Trend following

Using the same future contracts listed in Table 1, we also simulate a simple trend-following system in Figure 2. We use a medium-speed system which results in a turnover of six weeks [2]. Over the past 40 years, a portfolio of these 20 systems achieves a Sharpe ratio of one.

The absolute correlation of the returns from these systems are generally lower than the correlation of futures returns. This is an expected ‒ and desirable ‒ property of trend-following systems. Correlation has also been less affected by the 2008 credit crisis than the underlying assets.


Over the long term, we see evidence of markets becoming increasingly connected, especially within asset classes. More recently, the 2008 financial crisis stands out as a remarkable period – there is, however, tentative evidence that this “blip” is coming to an end.


[1] Correlation is defined as a 100-day exponentially weighted covariance divided by the 100-day exponentially weighted standard deviations, assuming a zero mean return for all assets. For each pair of assets we compute the correlations from the earliest date when both assets have started trading, excluding values in the warm-up period of 100 days.

[2] We define turnover as the ratio of the mean absolute position to the mean absolute five-day change in position, which roughly indicates how many weeks it takes to close a position, but ignores small trades back and forth.

This article contains simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity and cannot completely account for the impact of financial risk in actual trading. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results and all of which can adversely affect actual trading results. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any investment will or is likely to achieve profits or losses similar to those being shown.

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